Neural network classifier trained for purchasing differentiation

ABSTRACT

Systems and methods for self-checkout at a point-of-sale are provided. The system and method includes using a plurality of radio frequency identification (RFID) transceivers within a store, and an RFID reader configured to receive an RFID code from an RFID tag activated by the plurality of radio frequency identification (RFID) transceivers. The system and method also includes using a classifier configured to determine whether the RFID tag is inside or outside a designated area, wherein the classifier is trained in a manner that a number of items incorrectly identified as being purchased is below a threshold to minimize customer dissatisfaction (CDS) determined as the ratio of the value of items charged to the customer but not purchased by a customer to the total charge to the customer.

RELATED APPLICATION INFORMATION

This application claims priority to Provisional Application No.62/972,265, filed on Feb. 10, 2020, incorporated herein by reference inits entirety, and Provisional Application No. 62/993,838, filed on Mar.24, 2020, incorporated herein by reference in its entirety.

BACKGROUND Technical Field

The present invention relates to a point-of-sale checkout system andmore particularly a point-of-sale checkout system utilizingradio-frequency identification (RFID) and a neural network classifier.

Description of the Related Art

Customer self-checkout can involve manually aligning each productbarcode with a scanner, which also involves sequential handling of eachitem, since barcodes require line-of-sight (LoS) and proper positioningrelative to the scanner. This can result in delays and frustration dueto repeatedly searching for the barcodes and repeated attempts tosuccessfully scan the code. Such experiences can reduce the customer'soverall shopping satisfaction. Using handheld readers for self-checkoutleaves the responsibility of correctly scanning the products completelyand solely to the customer. Lengthy checkout processes, including boththe wait time in the queue as well as the manual process of scanningitems in succession can reduce customer satisfaction.

SUMMARY

According to an aspect of the present invention, a system is providedfor self-checkout at a point-of-sale. The system includes a plurality ofradio frequency identification (RFID) transceivers within a store, andan RFID reader configured to receive an RFID code from an RFID tagactivated by the plurality of radio frequency identification (RFID)transceivers. The system also includes a classifier configured todetermine whether the RFID tag is inside or outside a designated area,wherein the classifier is trained in a manner that a number of itemsincorrectly identified as being purchased is below a threshold tominimize customer dissatisfaction (CDS) determined as the ratio of thevalue of items charged to the customer but not purchased by a customerto the total charge to the customer.

According to another aspect of the present invention, a system isprovided for self-checkout at a point-of-sale. The system includes apair of RF-absorbing walls separated by a distance, and a plurality ofradio frequency identification (RFID) elevated transceivers within eachof the pair of RF-absorbing walls. The system further includes aplurality of floor-mounted transceivers in a floor below the pair ofRF-absorbing walls, wherein the signal coverage of the plurality ofradio frequency identification (RFID) transceivers determines a checkoutarea (CA), and an RFID reader, wherein the RFID reader is configured toreceive an RFID code from each of a plurality of RFID tags activatedsimultaneously by the plurality of radio frequency identification (RFID)transceivers, the RFID reader configured to receive each of theplurality of RFID codes in a separate time slot of a block of a transmitframe. The system further includes a classifier configured to determinewhether the RFID tag is inside or outside a designated area, wherein theclassifier is trained in a manner that a number of items incorrectlyidentified as being purchased is below a threshold to minimize customerdissatisfaction (CDS) determined as the ratio of the value of itemscharged to the customer but not purchased by a customer to the totalcharge to the customer.

According to yet another aspect of the present invention, a method isprovided for self-checkout at a point-of-sale. The method includesdetecting that a person has entered a checkout area, and determiningwhether an RFID tag is inside the checkout area (CA) using a neuralnetwork classifier and a plurality of radio frequency identification(RFID) transceivers. The method also includes charging the person anamount for items associated with the RFID tag determined to be insidethe checkout area.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a diagram illustrating a high-level system/method for apoint-of-sale checkout system utilizing radio-frequency identification(RFID), in accordance with an embodiment of the present invention;

FIG. 2 is a top view illustrating a system/method for a point-of-salecheckout system utilizing radio-frequency identification (RFID), inaccordance with an embodiment of the present invention;

FIG. 3 is a top view block diagram illustrating a checkout area and awaiting area for a point-of-sale checkout system utilizingradio-frequency identification (RFID), in accordance with an embodimentof the present invention;

FIG. 4 is a diagram illustrating a checkout process for a point-of-salecheckout system utilizing radio-frequency identification (RFID), inaccordance with an embodiment of the present invention;

FIG. 5 is another diagram illustrating a checkout process for apoint-of-sale checkout system utilizing radio-frequency identification(RFID), in accordance with an embodiment of the present invention;

FIG. 6 is a diagram of an RFID communication protocol for amulti-antenna tag-reading process, in accordance with an embodiment ofthe present invention;

FIG. 7 is a block/flow diagram of an RFID reader for the point-of-salecheckout system utilizing radio-frequency identification (RFID), inaccordance with an embodiment of the present invention;

FIG. 8 is a top view illustrating a section of a store implementing apoint-of-sale checkout system utilizing radio-frequency identification(RFID), in accordance with an embodiment of the present invention; and

FIG. 9 is a block/flow diagram illustrating a checkout process for apoint-of-sale checkout system utilizing radio-frequency identification(RFID), in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In accordance with embodiments of the present invention, systems andmethods are provided to/for a seamless self-checkout at a point-of-saleusing radio-frequency identification (RFID).

In one or more embodiments, a system and method identifies productsusing passive RFID tags. The system can scan the RFID tags associatedwith the products in a dedicated check-out area that is large enough toallow the customers to simply walk in and stand in the area until thescan is complete.

In one or more embodiments, the RFID-based seamless self-checkoutsolution does not require continuous tracking of customers with privacyintrusive cameras.

In various embodiments, the system can include a custom-built RFIDreader that simultaneously decodes a tag's response from multiplecarrier-level synchronized antennas, enabling capture of a large set oftag observations in a very short time, where carrier level refers to thecarrier frequency(ies). The synchronization can be very accurate and isup to the phase of the carrier frequency of the signal. The observationscan be fed to a neural network model that can accurately distinguish theproducts within the checkout area and in possession of the customer fromthose items and RFID tags that are outside the checkout area.

Various embodiments can use passive (i.e., battery-less) RFID tags asopposed to identifying products with cameras. RFID is prevalent inretail industry, especially for inventory tracking at major apparelbrands. Instead of continuously tracking customer activity throughoutthe store, the system can identify purchases in a dedicated area (e.g.,right before the store exit) referred to as a checkout area (CA) that isequipped with RFID antennas. The customers can simply walk into andstand in the CA, while carrying or carting the products they wish topurchase. In such a manner, the system should not require any extensivehuman involvement to calculate the cost of the purchased items.

In accordance with additional embodiments of the present invention,systems and methods are also provided to minimize vendor loss andmaximize the customer satisfaction by modeling the checkout system as abinary classifier with a ground set of objects. The ground set ofobjects can be partitioned into a two sets representing purchased items(e.g., the contents of the customer's cart) versus unpurchased items(e.g., the objects not in the customer's cart).

In various embodiments, a classifier algorithm partitions a set ofobjects into positive and negative subsets, where the set of objects caninclude all objects detected by a seamless self-checkout system at apoint-of-sale using radio-frequency identification (RFID). Thesedetected objects may include items in the possession of a personchecking out, as well as items in the possession of other people waitingto check out and items still about the store, but not in anyone'spossession (e.g., items still on shelves, returns, items left inunattended carts, etc.).

In accordance with embodiments of the present invention, systems andmethods are provided to measure vendor loss and customer satisfaction asa performance metric. For example, when the positive and negative setsof the classification problems are random variables, these metrics aredefined for a particular realization of the problem. In variousembodiments, the metrics are defined for a given classification problem,while not for a particular classifier, since different classifiers couldbe applied to the classification problem. The metric(s) is/are definedfor a set of item(s). The metrics are defined for this classificationproblem and can be computed for any classifiers and hence the value ofthe metric can be used to evaluate any particular classifier.

In various embodiments, the defined metrics can be used in evaluatingthe performance of a classifier, and also used directly in the design ofthe automatic/self-checkout system.

In various embodiments, an objective function can be defined based onthe metrics associated with vendor loss and customer satisfaction, andfor example may be used in training a model, for example, for amulti-layer neural network (e.g., deep neural network, recurrent neuralnetwork, etc.).

Embodiments described herein may be entirely hardware, entirely softwareor including both hardware and software elements. In a preferredembodiment, the present invention is implemented in software, whichincludes but is not limited to firmware, resident software, microcode,etc.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer readable medium may include anyapparatus that stores, communicates, propagates, or transports theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be magnetic, optical,electronic, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. The medium may include acomputer-readable storage medium such as a semiconductor or solid statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk and anoptical disk, etc.

Each computer program may be tangibly stored in a machine-readablestorage media or device (e.g., program memory or magnetic disk) readableby a general or special purpose programmable computer, for configuringand controlling operation of a computer when the storage media or deviceis read by the computer to perform the procedures described herein. Theinventive system may also be considered to be embodied in acomputer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner to perform the functions describedherein.

A data processing system suitable for storing and/or executing programcode may include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code to reduce the number of times code is retrieved frombulk storage during execution. Input/output or I/O devices (includingbut not limited to keyboards, displays, pointing devices, etc.) may becoupled to the system either directly or through intervening I/Ocontrollers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

It is to be understood that aspects of the present invention will bedescribed in terms of a given illustrative architecture; however, otherarchitectures, structures, substrate materials and process features andsteps can be varied within the scope of aspects of the presentinvention.

Referring now in detail to the figures in which like numerals representthe same or similar elements and initially to FIG. 1, a high-levelsystem/method for a point-of-sale checkout system utilizingradio-frequency identification (RFID) is illustratively depicted inaccordance with an embodiment of the present invention.

In one or more embodiments, a checkout area (CA) 110 of a checkoutsystem 100 can be defined by a one or more antennas placed in differentpositions and with different orientations to reliably readradio-frequency identification (RFID) chips (also referred to as “tags”)regardless of the chip's orientation, and configured to activate andread RFID chip associated with each item in a store. The customers cansimply walk into and stand in the CA 110 while carrying the productsthey wish to purchase. The antennas can be elevated transceivers 120 orfloor-mounted transceivers 125 that can both energize a passive RFIDchip and read a transmitted signal, where the RF tags can be readwithout line-of-sight (LoS) to the tag(s). In various embodiments, theelevated transceivers 120 can be a distance above the floor of the storeand situated at one or more heights aligned with the heights of itemsplaced in carrying bags, shopping carts, a customer's hands or arms, orother conveyances used to carry store items while shopping. A pluralityof elevated transceivers 120 can be placed at several different heightsto improve the reading of any particular RFID chip, regardless of heightor orientation of the one or more RFID chip(s) to improve performance ofthe checkout system 100. Different numbers and arrangements of theelevated transceivers 120 and floor-mounted transceivers 125 can beused. For example, two (2) or four (4) elevated transceivers 120 can beon the sides and two (2) floor-mounted transceivers 125 on the groundinside a waiting area, although other quantities are contemplated.

In various embodiments, the antennas can be positioned in three mainorientations defined by three planes that are mutually orthogonal. Inaddition, the antennas can be placed in various positions andsufficiently apart from each other to form uncorrelated communicationchannels. If a tag is in a deep fading spot or blind spot with respectto one antenna, it may not be for another antenna at a different laterallocation, height, and/or orientation. Deep fading happens when multiplecopies of a signal destructively combine at a particular location. Byutilizing antenna diversity for transmit (TX) and receive (RX) paths,the checkout system 100 can reduce the occurrence of blind spots. Usingdifferent orientations of the antennas also allows for reading tags inthe same physical volume but with different orientations.

In various embodiments, elevated transceivers 120 can be angled/orientedto scan a region outside of a designated checkout area (CA) 110,including a waiting area (WA).

In various embodiments, one or more transceivers can be floor-mountedtransceivers 125, where floor-mounted transceivers can be positioned indifferent quadrants of the checkout area 110.

In various embodiments, each of the transceivers 120, 125 can include atransmitter that sends a signal that can energize and activate an RFIDchip, and a receiver that can receive a signal transmitted by theactivated RFID chip. The signal transmitted from the activated RFID chipcan include a code that is unique to each item present in the store(each tag responds with its unique ID), so both the checkout system andan inventory system can identify the specific product being purchased,and deduct the item from a list of items available for purchase.

In various embodiments, dual area antenna can be located in differentplaces and with different orientations, such that the antennas can begrouped into two sets, a first set primarily reading the RFID tags thatare outside the checkout area and a second set for primarily reading theRFID tags that are placed inside the checkout area. A dual-area antennadeployment can be used to cover areas within and outside the CA 110,where two distinct sets of antennas can be placed in such a way that the“inside” antennas primarily illuminate and read the tags “inside” theCA, and “outside” antennas primarily illuminate and read the tags“outside” the CA. Using different orientation of the antennas canthereby cover different physical areas and read tags with differentorientations that are in the same physical volume but in differentorientations. It should be noted that the antenna of the transceivers120, 125 may transmit a weak signal in some unintended directions, soRFID tags outside the checkout area 110 may be activated and read by thesystem 100.

In various embodiments, RF-absorbing walls 130 can be positioned onopposite sides of a checkout area 110, where the walls can be positionedto accommodate one or more people, including a shopper with purchaseditems. The RF-absorbing walls 130 can assist in blocking radio frequency(RF) signals transmitted in unintended directions (e.g., outside theCA), where the RF-absorbing walls 130 can be lined with RFabsorbers/absorbing material(s) to minimize RF signal spillover from theantennas mounted inside the CA. The physical structure can also clearlydemarcate the checkout area 110, and contain multiple RFID antennascarefully placed to mitigate blind spots. The RF-absorbing walls 130 canbe shorter than an average person to provide sufficient vertical heightfor the elevated transceivers 120 without creating a claustrophobicenvironment.

In various embodiments, the RF-absorbing walls 130 can have a height ina range of about 2 feet (ft) to about 4 ft, or about 2½ ft to about 3½ft, or about 3 ft, although other heights are also contemplated. TheRF-absorbing walls 130 can have a thickness sufficient to house elevatedtransceivers 120, where the transceivers can be oriented at differentangles to cover the checkout area 110 and/or waiting area, as well astags in the checkout area 110 and/or waiting area with different heightsand/or orientations.

In various embodiments, multiple transceivers 120 can be placed indifferent lateral positions, heights, and with differentorientations/facings within each of two RF-absorbing walls 130 toreliably read tags regardless of the tag's position and orientation. Thecheckout area 110 can be situated between the two RF-absorbing walls130. The checkout system 100 can scan RFID tagged products withoutrequiring the customer to take them out of bags 150 or carts (if in acart or a bag) and align them with a specific antenna.

In a nonlimiting exemplary embodiment, six transceivers can be deployedas follows: (1) two elevated transceivers 120 in each side wall facingthe inside area, which are placed almost at 45° angle to the face of theabsorbing wall(s) 130 and (2) two floor-mounted transceivers 125 underthe floor mat placed parallel to the floor facing upward. Four antennascan also be deployed to read the tags in the waiting area (WA): (1) oneantenna can be mounted within each absorbing wall 130 at about a 20°angle facing the WA, and (2) two floor-mounted transceivers 125 underthe floor mat at a distance of about 2 ft from the entrance of RFGo.

In various embodiments, one or more persons 140 can enter the checkoutarea 110, where one or more of the persons can have one or more items tobe purchased. The one or more items can be held in bags 150, shoppingcarts, or in hand, for purchasing. The checkout system 100 can detecteach item within the checkout area 110, and determine a total cost forthe items identified within the checkout area without line-of-sight(LoS) to the tag(s). In some instances, items outside the checkout area110 may be identified by the checkout system as within the checkout areaand being purchased.

In various embodiments, the RF-absorbing walls 130 may not block theentrance and exit of the checkout area 110, so energizing signals fromthe transceivers 120, 125 may extend beyond the entry boundary 112 andexit boundary of the checkout area 110 to RFID tags outside the checkoutarea. The RF-absorbing walls 130 may not extend to the ceiling of astore, so energizing signals from the transceivers 120, 125 may extendpassed the RF-absorbing walls 130 to RFID tags outside the checkoutarea. Similarly, ID signals transmitted by the RFID tags outside thecheckout area 110 may be detected and read by transceivers 120, 125within the checkout area 110, and register the received signal asitem(s) being purchased by a customer. The RF tags can thereby be readwithout LoS to the tag(s).

In various embodiments, sensors may be situated to detect personsentering and/or leaving the checkout area 110, where there can be entrysensor(s) 160 to detect when a person has entered the checkout area, andan exit sensor 165 to determine when a person has exited the checkoutarea 110.

In various embodiments, the sensors 160, 165 can be photo-electricsensors, ultrasonic sensors, microwave/radar sensors, videocameras/sensors, infrared sensors, and combinations thereof.

FIG. 2 is a top view illustrating a system/method for a point-of-salecheckout system utilizing radio-frequency identification (RFID), inaccordance with an embodiment of the present invention.

In various embodiments, one or more sensor(s) can be situated in thefloor below the checkout area, where a floor sensor 170 can beconfigured to detect the presence of a person within the checkout area110. The floor sensor(s) 170 can extend over the entire checkout area110, a portion of the checkout area, or be in several different regionsof the checkout area to detect one or more persons in the checkout area.In various embodiments, the floor sensor 170 can be a pressure sensor.

In various embodiments, a determination that a person 140 has enteredand not left the checkout area 110 can trigger a checkout processes,including energizing and reading one or more RFID tag(s) identified aswithin the checkout area. The elevated transceivers 120 and/orfloor-mounted transceivers 125 can send out an energizing signal toenergize RFID tags within range of the transceivers 120, 125 to energizeand prompt the RFID tag(s) to send an ID code back to the antenna of thetransceivers. The checkout system 100 can scan RFID tagged productswithout requiring the customer to take them out of bags 150 or carts anddetermine a total cost without passing them in front of a scanner. Thefloor-mounted transceivers 125 can be used to identify an entry boundary112 and an exit boundary 117 of the checkout area 110.

FIG. 3 is a top view block diagram illustrating a checkout area and awaiting area for a point-of-sale checkout system utilizingradio-frequency identification (RFID), in accordance with an embodimentof the present invention.

In various embodiments, each customer brings the products (e.g., in abag, shopping cart, or by holding in hand) and stands briefly inside thecheckout area (CA) 110, while other customers line up in the waitingarea (WA) 210. The signals from the transceivers 120, 125 can extendbeyond the checkout area 110, including outside an entry boundary 112and an exit boundary of the checkout area 110. The waiting area 210 canbe offset a distance, D, from the checkout area 110, where the distance,D, can be sufficient to reduce activation of RFID tags outside thecheckout area 110, where passive (i.e., battery-less) RFID tags areused. The waiting area 210 can provide room for customers to wait andmaneuver into the checkout area.

In various embodiments, the waiting area can be a distance, D, of about1 ft to about 5 ft, or about 2 ft to about 4 ft, or about 1 ft to about2 ft, although other distances are also contemplated.

The checkout system 100 can identify purchases in a dedicated area rightbefore a store exit with RFID antennas. In various embodiments, thecheckout system 100 can completely and solely identify the productspresent in the CA, meaning no product is missed from the currentcustomer's basket or extra products are identified (e.g., from otherpeople's basket in the WA).

In various embodiments, the performance of the checkout system 100 canbe based on Recall and Precision, where Recall is the ratio of correctlyidentified products to all products in the customer's basket (ideal is100%), and Precision is the ratio of correctly identified products toall products identified by the checkout system 100 (again ideal is100%), which may include those inside the CA (correct detection) as wellas outside the CA (incorrect detection).

In various embodiments, a dual-area antenna deployment can be used tocover the area both within the CA 110 and the WA 210 to increase thecheckout accuracy. A software classifier can then determine whether atag 410 is inside or outside the CA 110 based on the observationsgathered by the checkout system 100 (without explicit tag localization).

In various embodiments, the checkout area (CA) 110 can have dimensionsof about 2 ft by about 2 ft to about 10 ft by about 10 ft, or about 3 ftby about 4 ft to about 5 ft by about 8 ft, although other sized areasare also contemplated.

In various embodiments, the waiting area (WA) 210 can have dimensions ofabout 3 ft by about 10 ft to about 20 ft by about 20 ft, or about 5 ftby about 8 ft to about 15 ft by about 15 ft, although other sized areasare also contemplated.

The CA 110 and WA 210 can each be large enough to comfortablyaccommodate a customer with products to be purchased at checkout, alongwith bags, carts, children, etc. The CA can allow some freedom ofmovement to the customer without forcing them to stand still or in aparticular position/orientation during the checkout process.

In a non-limiting exemplary embodiment, the CA 110 can be unbarricadede.g., not enclosed by sliding doors or other large physical obstacles,where this can be desirable both for aesthetics, where the CA does notabruptly stand out as a major structure in an open-floor store design,and for human psychology where customers do not feel trapped from allsides. The CA 110 can form a “lane” demarcated by two waist-level sidewalls without entry or exit barriers. A waiting area (WA) 210 can beadjacent to the CA 110.

FIG. 4 is a diagram illustrating a checkout process for a point-of-salecheckout system utilizing radio-frequency identification (RFID), inaccordance with an embodiment of the present invention.

In various embodiments, an RFID chip 410 can be associated with aparticular item by being attached to or embedded in the packaging ofeach item. Each item in a store can have an associated RFID chip with aunique ID value that can identify the type, brand, size, etc. of productbeing purchased, and corresponding to the price of the item. In variousembodiments, the unique ID value can specifically identify a single itemfrom multiple items of the same type, brand, size, etc. The RFID tags410 can be passive, where the chip does not include a battery, butinstead uses an antenna that can be energized by a radio signal. Invarious embodiments, an RFID chip 410 can be active (include a powersource (e.g., battery).

When a customer takes an item and places it in a bag, cart, or simplyholds it, the RFID tag accompanies the item around the store, and mayeventually reach the checkout area 110. When the customer enters thecheckout area 110, one or more sensors 160, 170 may determine thecustomer is inside the checkout area and wishes to pay for the itemspresent. The checkout system may then activate the antennas and send anenergizing signal to the passive RFID tags 410.

In various embodiments, the checkout system 100 can include a readerthat energizes the RFID tags 410 and sends commands to them. Afterharvesting enough energy from the reader, tags wake up to listen for thereader commands and react to them, for example, by sending a particularresponse to the reader. Signals from various transceivers can reachmultiple tags 410, where the tags are not in a blind spot. The receivedradio frequency signals can activate the tags 410 causing each RFID tagto broadcast a checkout code back to the antennas of one or moretransceivers 120, 125.

FIG. 5 is another diagram illustrating a checkout process for apoint-of-sale checkout system utilizing radio-frequency identification(RFID), in accordance with an embodiment of the present invention.

In various embodiments, multiple antennas (elevated transceivers 120and/or floor-mounted transceivers 125) can be used, so no product ismissed from a customer's basket within the checkout area 110, nor extraproducts from outside the checkout area being identified as purchased bythe customer. Multiple observations from a single RFID tag's reply canbe obtained by simultaneously decoding the reply from multiple antennas(elevated transceivers 120 and/or floor-mounted transceivers 125) thatare synchronized at the carrier-level. Enough readings from multipleantennas placed in different positions can be utilized to accuratelymake a determination. Multiple antennas with sufficient power may readthe tags within the CA 110, but there may be signal spillover to areasoutside the CA resulting in unwanted reads. This problem may beexacerbated for an unbarricaded CA. The checkout system may thenactivate the antennas and read a checkout code transmitted by the RFIDtag.

A neural network-based classifier can extract features from theobservations and combines them appropriately using supervised learningcan be used to identify items being purchased within the CA 110.

In various embodiments, the fast identification process can take only afew seconds that is the typical time for credit card and phone-based (QRcode, NFC) payment authentication, or facial-recognition based payments,etc. In various embodiments, a checkout process that reads the RFID tagson the items being purchased can take a time period of about ½ seconds(sec) to about 60 sec, or about 5 seconds (sec) to about 60 sec, orabout 10 sec to about 30 sec, or about ½ sec to about 5 sec, or about ½sec to about 2 sec, although other time periods are also contemplated.

A stationary tag in a particular position and orientation is in a blindspot when it is not readable at all. Blind spots occur either due toweak illumination, where the tag is not activated by the reader and soit does not even reply to it; or to weak response, where tags respondbut the replies are not decodable at the reader. For a passive RFID tag,being illuminated means that the tag can harvest enough energy to wakeup and respond to reader commands. The received signal energy at a tagis a function of the transmit power of the reader, the distance betweenthe tag and the reader, the direction of the tag relative to the readerantenna, the reader antenna pattern and environmental factors such astemperature. RFID tags are best read when they are oriented in a waythat maximizes the received power induced at the tag's antenna. For aflat tag, this can happen when the received signal from the reader isorthogonal to the plane of the tag's antenna.

Any deviation from this ideal orientation may significantly reduce thesignal strength of the tag's response In addition to waking up, a tagneeds to receive reader commands with enough signal-to-noise ratio (SNR)to decode and respond to them.

The signals backscattered by other tags in the vicinity can also impacta particular tag's readability. For example, a tag that can be detectedwhen it is the only tag in the reader's view may not be readable whenother tags are brought in close proximity.

A tag may not be illuminated by the reader when it is situated in a spotwith deep fading due to destructive interference. Deep fading happenswhen multiple reflected copies of a signal destructively combine at aparticular location. Similarly, the same destructive interference mayalso happen at the reader antenna for the reflected copies of a tag'sresponse affecting its reception and/or decodability by the RFID reader700.

In various embodiments, the checkout system includes a custom-built RFIDreader 700, that is in communication with the transceivers 120, 125, andextracts multiple observations from a single tag's reply bysimultaneously decoding the reply from multiple antennas that aresynchronized at the carrier-level (carrier frequency).

In contrast, traditional RFID readers may decode tag replies using oneantenna at a time, which can require significantly longer time tocapture the same number of observations due to sequential energizing andreading of RFID tags. Reading a tag may not only obtains its ElectronicProduct Code (EPC) (defined in the EPCglobal Tag Data Standard), butalso reveals important features that may be extracted from its physicalsignal (e.g., signal strength).

FIG. 6 is a diagram of an RFID communication protocol for amulti-antenna tag-reading process, in accordance with an embodiment ofthe present invention.

A response from a particular tag may not be decodable by the RFID reader700 when other tag(s) also respond at the same time due to collision.Although, given enough time, a random access protocol may resolvecollisions, a long resolution time is not desirable. Since a typicalretail store may have a large population of tags near the CA 110 (e.g.,other tags on the shelves or tags carried by other customers in the WA210), the tags inside the CA may experience a high number of collisions,which can be due to multiple tags responding at the same time. Thisreduces the probability of decoding a particular tag in a given timeslot and extends the time required to discover all the tags within theCA.

In various embodiments, the RFID reader 700 can instruct thetransceivers 120, 125 to broadcast a query command and indicates thenumber of available slots; if a tag 410 can decode the query, it choosesa random slot and later responds with a 16-bit random sequence (calledRN16) using FM0 modulation, which is a particular modulation scheme inthe context of RFID generation 2 standard. in the selected slot; in eachslot, if the reader can decode an RN16, it sends an acknowledgment (ACK)containing the same decoded RN16; and each tag decoding the ACK matchesthe included RN16 to the RN16 it chose earlier, and replies with its EPCwhen there is a match. A pair of TX/RX RF chains that share the samelocal oscillator can be employed for coherent detection.

Since each tag randomly chooses a slot to reply, a slot may have noRN16, only one RN16 or multiple RN16s that collide at the reader. Incontrast, since it is unlikely for two tags to generate the same RN16,EPC responses do not usually collide. RN16s do not have built-in errordetection, making it difficult for the reader to know whether an RN16was decoded correctly or not. Leveraging the spatial diversity acrossantennas allows better decoding a valid RN16 even when collisionshappen.

In various embodiments, transmission from an antenna comprises transmitframes (TF) where each TF contains several time slots for querying thetags. A block 600 of transmit frames having time slots 630, where slot iof TF k is denoted by S_(i) ^(k). Rows 610 show time slots for responsesstimulated by a first transceiver, whereas columns 620 represent slots.

In a nonlimiting exemplary embodiment, during the first TF, a firstantenna is the active transmitter and triggers responses from tags x₁and x₂ in slot S₃ ¹, and in the second TF, second antenna is the activetransmitter and triggers responses from the same tags in slot S₄ ². Intime slot, S₃ ¹, the reader decodes two RN16s transmitted by x₁ and x₂using the received signal at antenna RX₂ and RX₄, respectively.

In various embodiments, when the same antenna is used both as TX and RX,the tag with stronger received reply (say due to its proximity to theantenna) is always favored where the same set of stationary tags collidein the same time slot.

FIG. 7 is a block/flow diagram of an RFID reader for the point-of-salecheckout system utilizing radio-frequency identification (RFID), inaccordance with an embodiment of the present invention.

In various embodiments, a reader 700 can include a plurality of softwaredefined radio (SDR) platforms 710 for wireless communications (e.g.,USRP X310), a Clock Distribution Module 720 (e.g., octoclock) for amulti-channel system synchronized to a common timing source, a pluralityof matched filters 730 for maximizing the signal-to-noise ratio (SNR), aplurality of gate blocks 740, a plurality of tagged decoders (TD) 750, aswitch 760, a Reader block 770, an output X310 780, which is a softwaredefined radio board, a Rasberry Pi 790, and a multiplexer 795.

Signals from each transceiver 120, 125 can be received by the X310 Eachsoftware defined radio (SDR) platform 710 can be equipped with either aUBX or TwinRX daughterboard that allows for communication within 902-928MHz, an operating band of UHF RFID tags 410. The reader 700 can be inelectronic communication with six antennas in the CA and four antennasin the WA for a total of ten antennas. Each antenna can separate the TXand RX signals with a circulator. Five TwinRX daughterboards can be usedto enable ten RX RF chains, where each RF chain can be connected to oneantenna. The output of the UBX can be connected to a set ofmultiplexers, which is controlled by a Raspberry Pi 790 to select theactive TX antenna. A received I/Q sample can pass through a finiteimpulse response (FIR) matched filter 730 (MF) to mitigate the impact ofnoise. Then, the Gate block (G) 740 can perform pulse detection to docoarse frame synchronization. The frame can then be forwarded to the TagDecoder block (TD) 750, which aligns symbols using preamble-basedcorrelation and decodes the RN16 and EPC packets. The TD block can beextended to compute the Interference metric (IM) and estimate thereceived signal strength (RSS), where an interference metric can bebased on a mean and a variance of a set of differences between twosymbols in a bi-phase decoder after equalization. The TD blocks 750 canforward the decoded RN16s along with the IM to a central block calledthe Switch 760. The Switch block 760 implements the RN16 selectionpolicies 799 and forwards the selected RN16 sequence to the reader block(R) 770, which generates the ACK based on the forwarded RN16. Theselection policy 799 input into the switch 760 determines if any of thedecoded RN16 signals are valid, and which RN16 should be picked to sendan acknowledgement signal. The Reader block 770 can also be responsiblefor generating the Query packets broadcast to the tags. The Switch block760 can also keep track of the current slot and Frame indices to updatethe transmitter in time. To do so, Switch block 760 can establish a UDPconnection with an external Raspberry Pi 790 that operates as amicro-controller to activate specific ports of the multiplexers 795.Once the end of the TF is reached, the Switch 760 can encapsulate thenext selected port within the UDP packet and send it over to themicro-controller.

Accurate localization may not be practical in retail settings due to thenon-stationary environment. Combining observations, such as several RSSIand number of readings, from multiple antennas deployed in RFGo couldyield a reliable estimation of the tag's position.

Antenna diversity for the TX path can utilize Time Division MultipleAccess (TDMA), where it activates each antenna in turn to addressillumination issues. Collisions occur during the RN16 phase where tagsrandomly choose the same time slot. By leveraging parallel decoding 2 ofthe received RN16 signal at multiple antennas and analyzing low-levelsignal metrics, our reader picks the RN16 with the highest probabilityof being correctly decoded. If a “poor” RN16 is ACKed in the absence ofsuch an intelligent policy, the subsequent EPC response from the tagwould not be decoded and the slot would be wasted.

Independent and synchronized RX RF chains collect I/Q samples inparallel, which are then used to retrieve RN16s.

In various embodiments, a Fixed Antenna Policy (FP) can rely on no metainformation from the received signals. FP uses a fixed RX antenna, forexample, RX_(j) for all the slots in TF_(j), to decode an RN16 and togenerate and transmit the acknowledgement (ACK). A special case of FP,where RX and TX antennas are the same, can capture the behavior oftraditional readers that are not equipped with simultaneous decodingcapabilities. Note that, despite limitation of FP on RN16 selection,simultaneous decoding of EPC from multiple RX antennas can still happenin the reader 700. Simultaneous decoding not only increases thelikelihood that a tag reply will be decoded through receive diversity,but it also provides multiple readings (or observations) for the tag(same EPC but different signal features) when it can be decoded frommultiple antennas.

In various embodiments, a Majority Voting Policy (MVP) can use theknowledge of the set of decoded RN16s. MVP selects the RN16 that has themaximum number of detection events among all the ones decoded by theantennas. If two or more RN16s have been decoded equal number of times,MVP picks one at random. In various embodiments, each of the decodedRN16s from the plurality of antennas in a single tag reading sessionreceive votes based on the number of antennas that decoded the decodedRN16s, and an antenna which corresponds to the decoded RN16 which has amaximum number of votes is selected.

In various embodiments, an Interference Metric Based Policy (IMP) canuse the signal attributes for the set of decoded RN16s. In this policy,we compute a specially designed interference metric (IM), formallydefined in Sec. 4.5, for each of the simultaneously received signals. IMprovides a measure of interference in the received signal. Thereby, IMPselects the RN16 that has the minimum IM across all decoded RN16s,corresponding to the RN16 with highest decoding probability. Similar toMVP, IMP may also be randomized to select one RN16 among all RN16s forwhich their IM is below a threshold.

In resolving collisions when multiple tags reply at the same time slot,the decoder associated with only one of the RX antennas is capable ofcorrectly decoding an RN16, then with very high probability, the decodedRN16 by different antennas are all different.

When multiple replies from different tags are colliding, there is a goodchance that these two RN16's belong to two different tags and hence aredifferent.

Interference metric (IM) can estimate a packet decoding rate (PDR) of anRN16 sequence in the absence of explicit error detecting mechanisms. IMestimates the fraction of interference and noise power in the receivedsignal without explicit knowledge of the received signal, theinterferers, or their powers. PDR is then calculated based on IM asdescribed below. The lower the IM, the higher the probability of correctdecoding.

Given that an RN16 is either correctly decoded or not in a give slot,its effectiveness may be quantified by: the accuracy of a binaryclassifier that compares this measure with a threshold in order toclassify the received RN16 signals into two classes of “decodable” vs.“not decodable”. The accuracy of a binary classifier is defined as theratio of number of correct decisions over total number of decisions.Hence, as a quantitative representation of the true effectiveness of ameasure, the effectiveness of a measure can be defined as the maximumaccuracy that can be achieved for any threshold-based classifier whichcompares this measure with a threshold to define the outcome in one oftwo classes. In a threshold-based classifier, the classification isperformed by calculating a metric for each item. The metric functionoutputs a real value number. This number is then compared with athreshold value (a real number) to determine if it is larger or smaller.The item is declared to belong to a class based on the comparison of themetric function value for each items and at least a threshold. Therecould be more than one threshold and hence multiple classes.

A simple measure is Received Signal Strength (RSS) which is readilyavailable and can coarsely estimate PDR. Through a simulation involving32 tags and a frame size of 16 slots, we see that RSSI has a loweffectiveness of 0.69 which may be explained as follows. Since RSSmerely represents the total received energy, it cannot distinguishbetween a decodable signal without interference vs. the superposition ofmultiple low power interferers since both cases yield a high RSS.

If prior knowledge of the signal and the interferers is available, moresophisticated measures such as signal to interference and noise ratio(SINR) may be used under Gaussian distribution. Indeed, SINR achieves aneffectiveness of 0.97 in the same simulation since it directly capturesthe power of the useful signal relative to all interference and noise.

In various embodiments, the RFID reader 700 decodes a single RN16 packetwith highest received power among all interfering RN16 signals bytreating the interfering RN16s as noise. After equalization and passingthe received samples through a matched filter, an estimation of theFM0-modulated RN16 symbols can be constructed, which is then used fordecoding. A difference sequence can be determined by subtracting theeven numbered symbols from the odd numbered ones, e.g., (1st symbol−2ndsymbol) followed by (3rd symbol−4^(th) symbol) and so on. IM is then theratio of the standard deviation to the mean of the absolute value of thedifference sequence while the RN16 bits can be retrieved by comparingthe absolute value of the difference sequence with a threshold.

IM is a physical measure obtained in an intermediate step of thedecoding and hence its computational complexity is negligible. Theinterference metric can be based on the mean and variance of a set ofdifferences between two symbols in a bi-phase decoder afterequalization.

To determine how such features should be combined, supervised learningcan be employed by training a multi-layer neural network using theTensorFlow library in Python. Multiple tags can be positioned in the CA110, as well as several tags outside the CA in arbitrary positions andorientations. Readings can be captured from the tags and each featurevector fed into the neural network by labeling the feature vector aseither corresponding to an “inside” class or an “outside” class. Theneural network (i.e., model) then computes and stores the optimalweights for each of its connections by minimizing the decision error asdefined by a loss function. After training, the model applies theweights to an input feature vector and outputs the probability of a tagbelonging to the “inside” class. This is achieved by using a Sigmoidactivation function that outputs a value between 0 and 1. If the modeloutputs a value greater than 0.5 for a tag's feature vector, theparticular tag can be considered as inside the CA. In variousembodiments, the classifier can be trained with a multi-element featurevector for each tag including: the number of readings and average RSSIfor each of the TX/RX antenna pairs inside the CA.

FIG. 8 is a top view illustrating a section of a store implementing apoint-of-sale checkout system utilizing radio-frequency identification(RFID), in accordance with an embodiment of the present invention.

In various embodiments, customers can walk into the checkout area (CA)110 from a defined “entrance,” stay inside for a very brief time, andleave from the opposite side defined as the “exit.” During the time thecustomer stays inside the CA 110, the checkout system 100 can utilizethe custom-built RFID reader 700 to collect tag readings from severalRFID antennas of the transceivers and use a neural network to classifythe RFID tags as “inside” and “outside” the CA.

In various embodiments, items with RFID tags 410 may be stacked and/ordisplayed on shelves 310 in areas of the store adjacent to the checkoutsystem 100, where the RFID tags may be activated and read by thetransceivers of the checkout system 100. The antenna of the checkoutsystem 100 may have an ill-defined range and coverage area 810 thatoverlaps with portions of a waiting area 210 and shopping area includingthe shelves 310.

In a non-limiting exemplary embodiment, when a customer enters the CA, apair of infrared (IR) sensors 160 can detect the motion and trigger thestart of new session at the reader 700, which starts collecting tagreadings. Upon analyzing the readings, the neural network classifiesthem and displays the items detected in the CA on a billing terminal820. The customer can then pay using a credit card reader or QR codescanner of payment processor of the billing terminal 820 and leaves thecheckout lane. Another pair of IR sensors 170 can monitor the exit,enabling the system 100 to accept the next customer and start a newcheckout session. Visual indicators 830 can guide the customer throughthe operation of each session, for example, a visual display screen,lights, sounds, verbal instructions, and combinations thereof.

The sequence of operations including the customer entry, scanning andoutput of the classification is collectively referred to as a checkout“session.” In various embodiments, the checkout system 100 may completethe checkout process right at the checkout counter 820 and provide afinal receipt to the customer before the customer exits the store. Invarious embodiments, the checkout system 100 may complete the checkoutprocess after the customer exits the store, and provides a final receiptto the customer electronically. In various embodiments, the checkoutsystem 100 may provide a tentative but not yet final charge at thecheckout counter with the possibility of adjustment within a time periodafter the customer exits the store. In various embodiments, the checkoutsystem 100 may provide a final receipt to the customer electronicallysome time up to the next opening of the store in the following day. Eventhough, the final adjustment to the customer card may have been madeduring a time period after customer checkout and the final bill has beenprovided to the customer, any undercharged or overcharged items may beadjusted during an inventory that is performed at night before the storeopens for the following day. Such adjustment could be very useful insaving a customer a trip to the store to visit the customer servicedesk. The customer using such checkout process may have agreed to suchscenarios, and may have allowed the adjustment to their original form ofpayment.

FIG. 9 is a block/flow diagram illustrating a checkout process for apoint-of-sale checkout system utilizing radio-frequency identification(RFID), in accordance with an embodiment of the present invention.

At block 910, a customer can enter a checkout area and be detected bythe checkout system. The checkout system 100 can determine that thecustomer is remaining in the checkout system and wishes to purchase oneor more items.

At block 920, the checkout system 100 can initiate reading RFID tags 410associated with the one or more items by sending a signal from theantennas associated with one or more of transceivers 120, 125, where thesignal broadcast by the transceiver(s) energize one or more RFID tags.The energized RFID tags can be in the checkout area or may be outsidethe checkout area.

At block 930, activated RFID tags send a coded response to the one ormore transceivers, including an ID identifying the item being purchased.

At block 940, the coded response can contend for and be received in aparticular time slot recognized by the RFID reader 700.

At block 950, the checkout system 100 determines which items are in thecustomer's possession and intended to be purchased from the receivedRFID responses and IDs. A classifier detects the items that a customerbrings to the CA 110 and differentiates the customer items from itemsoutside the CA.

At block 960, the customer is charged for the cost of the detecteditems, where the charge may be displayed to the customer orautomatically billed to a credit or debit card on file with the store.

At block 970, an indicator signals to the customer that the transactionis complete, and the customer exits the CA. The sequence of operationsincluding the customer entry, scanning and output of the classificationcan be collectively referred to as a checkout session 900.

In various embodiments, control sensors and visual indicators can guidethe operation and sequence of each session.

In a nonlimiting exemplary embodiment, when a customer enters thecheckout area (CA), a pair of infrared (IR) sensors can detect themotion and trigger the start of a new session at the custom-builtreader, where the reader can start collecting RFID tag readings. Uponanalyzing the readings, the neural network classifies them and displaysthe items detected in the CA on a billing terminal. The customer canthen pay using a credit/debit card reader or QR code scanner, and leavesthe checkout area. Another pair of IR sensors can detect the exit andreset the system/reader, enabling the system to accept the next customerand start a new checkout session. The IR sensors can also identifyundesirable scenarios, i.e., customers invading the CA while busy orentering the CA prior departure of previous customer, and notify thesystem.

In various embodiments, a software classifier detects items that acustomer brings to the CA 110. Since tags cannot be precisely localizedin typical retail environments, a neural network model can be used todistinguish the tags inside the CA 110 from tags outside the CA bylearning from the signal features exposed by the reader 700. The reader700 can simultaneously decode the same EPC on all RX antennas (e.g, N RXantennas, where N is the number of antenna) for each TX antenna. After NTX antennas are activated, the reader can extract N² features for eachtag from all TX/RX antenna pairs (transceivers). In addition to the richfeature vector, the dual-area antenna deployment helps us captureobservations from both tags inside the CA and the ones in the WA, givingthe NN model even more information to learn from.

In various embodiments, the items in a store, including the items aperson brings to be checked out, can be divided into two subsets; theitems the person is purchasing, and the items not being purchased. Thecheckout system, however, may not be able to differentiate between thetwo subsets in an entirely accurate manner. Therefore, the checkoutsystem may accurately identify a plurality of items that the person ispurchasing, but may also identify items that the person is notpurchasing as being purchased. This can be classified as true positivesand false positives. False positives can reduce customer satisfactiondue to added costs on the amount charged for items not purchased, aswell as reduced satisfaction due to the inconvenience of having to spendtime correcting such mistaken identifications.

In addition, the checkout system may accurately identify a plurality ofitems that the person is not purchasing, but may also misidentify itemsbeing purchased as not being purchased. This can be classified as truenegatives and false negatives. False negatives can increase vendorlosses due to items leaving the store without being charged. This canresult in lost profits and unaccounted for loss of inventory (i.e.,shrinkage).

In various embodiments, a classifier can partition the set of objectsinto two subsets: R for retrieved items that represent the P (i.e.,positive) class and NR for not retrieved items that represent N (i.e.,negative) class. The intersection of P with R and NR can be divided intotrue positive (TP) and false negative (FN), respectively. Theintersection of N with R and NR can be divided into false positive (FP)and true negative (TN) respectively.

A true positive (TP) is a classification of an item as being checked outby a customer when it is actually being checked out by a customer. Atrue negative is a classification of an item as not being checked out bya customer when it is actually not being checked out by a customer. Afalse positive is a classification of an item as being checked out by acustomer when it is not being checked out by a customer. A falsenegative is a classification of an item as not being checked out by acustomer when it is being checked out by a customer. Larger falsepositive values can reduce customer satisfaction. Larger false negativevalues can increase vendor losses.

In various embodiments, metrics can be defined to evaluate andillustrate the performance of the binary classification problem. Themetric can include the True Positive Rate (TPR, also referred to asSensitivity or Recall), defined as:

the ratio r=|TP|/(|TP|+|FN|),

where |TP| is the size of TP and |FN| is the size of FN.

The False Negative Rate (FNR) is defined as:

|FN|/(|TP|+|FN|),

to measure how well the classifier can find the actual positive class.

The True Negative Rate (TNR, also called Specificity) is defined as:

s=|TN|/(|TN|+|FP|),

and its complement, i.e., (|FP|/(|TN|+|FP|) is the False Positive Rate(FPR) to measure how well the classifier finds the negative class.

The Precision, also referred to as the Positive Predictive Value (PPV)is defined as:

p=|TP|/(|TP|+|FP|),

in order to measure how well the classifier can distinguish an item inthe positive class. The complement of precision, i.e., |FP|/(|TP|+|FP|),is called the False Discovery Rate (FDR).

As can be seen, the closer the value of the ratio(s) to 1, the fewer thenumber of false positives or negatives, and the more accurate the systemis. The greater the and Precision, the greater the customersatisfaction. The lower the False Discovery Rate, the greater thecustomer satisfaction. The greater the Sensitivity and Specificity, thelower the losses and the greater the profits.

The Negative Predictive Value (NPV) is defined as:

n=|TN|/(|TN|+|FN|), and its complement,

|FN|=(|TN|+|FN|), defines the False Omission Rate (FOR).

In various embodiments, when the performance of the classifier is ofinterest, the fact that the average metric will be available renders thedefinition of recall unusable even in the scenarios that the valuefunction is simply the size of the set, i.e., all items have equalvalue. All items can be treated the same, while some items may havedifferent worth or value, e.g., monetary value.

In various embodiments, the vendor loss (VL) and customer satisfaction(CS) can be defined for the classifier, as the mean of VL and CS takenover all possible realizations, which can be computed when thedistribution of the items for such realization is known, where arealization refers to on outcome of a particular experiment.

The value of the items that wrongfully have not been positively counted(when the items belong to the positive set) as well as the value of theitems that wrongfully have been positively counted (when the itemsbelong to the negative set) can be determined.

In order to understand the physical meaning of VL and CS consider ascenario where the class P represents the set of items that a customerbrings to the checkout station to purchase, and R is the set of itemsfor which the customer is charged by the classifier, which retrieves thepartition R in effort to find the class P. Partition R represents theset of items in R, where the classifier does not know the actual trueclass, P, that is the items that customer brings to the checkout area.The classifier however finds the set R and charges the customer for thisset. The classifier is perfect if each time it finds a set R that isequal to set P, that is R=P.

The vendor loss is defined as the ratio of the item prices that have notbeen charged by the system to the total amount for which the customer ischarged. Let f(A) represent the sum of the prices of the items in a set,A, where A can represent the set of items present in the store availablefor purchase. For example, where A is the set of all items for sale inthe store, the checkout system identifies a subset, P (positives), ofthe set, A, that is expected to represent the items the customer has andwill be charged for. f(A) can represent the sum of the prices of all theitems in set, A.

f(A)=Σv _(i), where v _(i) is the value of an item, a ₁ ∈A, and i is anindex over all items in set A.

VL=f(FN)/(f(TP)+f(FP)),

where f(FN) is the value of the items in the set of False Negatives(items taken from the store, but not identified by the classifier asincluded in set P), f(TP) is value of the items in the set of TruePositives (items taken from the store and paid for), and f(FP) is valueof the items in the set of False Positives (items charged to thecustomer, but not actually purchased). The functions, f, can be wheref(FN) is counting the number of items that are in the set FN, or wheref(FN) is the total worth of the items in set FN.

The vendor loss, VL, may also be defined as the ratio of the sum of itemprices that have not been charged by the systems to the total amountthat the customer has been charged after the adjustment for the itemsthat were not in the customer cart but was charged to the customer bythe system, as false positives. This implies that the customer hasclaimed an adjustment for mischarged items, or the system has recognizedthe error and adjusted it without customer interaction. In this case,the adjusted vendor loss (VL′) is defined as:

VL′=f(FN)/f(TP) because the price of the overcharged item, i.e., f(FP)is adjusted and removed from the total customer payment.

The customer dissatisfaction (CDS) is defined as the ratio of the priceof the items that have been charged to the customer without being in thecustomer's possession at checkout over the total amount for which thecustomer has been charged. Although this could be claimed by thecustomer and adjusted, but it could, nevertheless, makes the customerunhappy due to the mistake and associated inconvenience. The customerdissatisfaction may be reduced somewhat if the system automaticallydetects and adjusts the price in a later time, but before the customerrecognizes and attempts to correct the mistake (e.g., before a creditcard charge is received).

The CDS corresponds to:

${{CDS} = {\frac{f({FP})}{{f({TP})} + {f({FP})}} = {\sum{{v\;}_{j}/\left( {{\sum v_{k}}\  + {\sum v_{j}}} \right)}}}},$

where j and k are indexes representing items in subsets j charged to thecustomer without being in the customer's possession at checkout, and kfor items charged to the customer that were in the customer's possessionat checkout, where v_(j) and v_(k) as the value (worth) of the item(s)a_(i) and a_(k), respectively.

In various embodiments, CDS is representative of the total value of theitems that have been wrongly charged by the system over the total saleprice (equivalently percentage of the overcharging a customer in termsof the price not the number of items).

In various embodiments, the customer satisfaction (CS) is defined as theratio of the price of the items that existed in the customer cart andhave been charged to the customer over the total amount for which thecustomer has been charged.

${{CS} = \frac{f\left( {TP} \right)}{{f\left( {TP} \right)} + {f\left( {FP} \right)}}},$

which means that CS may be expressed based on CDS as CS=1−CDS. Thismeans that if the total charge is the same as the value of the items inthe customer cart, the customer is fully satisfied and CS=1.

If the customer is charged more than the value of the items in his cart,the customer satisfaction drops proportionally based on the value(s) ofthe mischarged items, and not just the number of mischarged items. Aperson mischarged for a few pounds of steak may be expected to be moreupset than a person mischarged for a candy bar out of an entire cart ofgroceries. The same can be expected to be true for the definition of CSbased on adjustment of the price of the items that the customer has notpicked up and did not exist in his cart.

In various embodiments, the customer may still remain fully satisfiedwith this definition of CS even if there exist some extra items in theperson's cart that were not charged for. These items can affect thevendor loss (VL) and appear as a loss to the vendor but would not beexpected to affect the customer satisfaction based on CS or CDS.

It should be noted that VL, CDS, and CS are defined based on the valueof the items not the number of items.

If an average value is used for all items, the calculations can revertback to Precision and Recall.

The definition of VL and CS metrics for a binary classification problemmay be extended to the classifier. A classifier can be defined over astochastic set of input of positive and negative sets. Instead offocusing on a particular realization of the classification problem andcomputing the VL and CS for that particular instance of the problem, allpossible such outcomes and its probability distribution are consideredto compute the expected value of VL and CS. The VL and CS for aclassifier can be defined as an average value of these metrics over allpossible realization of the classification problem and is denoted by VLand CS, respectively. Even though this averaging may be defined asregular expected value of the VL and CS for each realization of theproblem, it can be related as follows:

$\overset{\_}{CDS}*=\frac{{\mathbb{E}}\left( {f\left( {FP} \right)} \right)}{{\mathbb{E}}\left( {f\left( {{TP}\bigcup{FP}} \right)} \right)}$$\overset{\_}{VL}*=\frac{{\mathbb{E}}\left( {f\left( {FN} \right)} \right)}{{\mathbb{E}}\left( {f\left( {{TP}\bigcup{FN}} \right)} \right)}$$\overset{\_}{CS}*=\frac{{\mathbb{E}}\left( {f\left( {TP} \right)} \right)}{{\mathbb{E}}\left( {f\left( {{TP}\bigcup{FP}} \right)} \right)}$

The operator E means the expected value.

In various embodiments, the performance of the classifier is related tothe combination of CS and VL. Therefore, classifier design objective canbe to maximize CS and minimize VL. The CS * shows the averagesatisfaction of the customers in terms of the value function defined onthe set of items, hence, it represents the fraction of the item valuesfor which the customer is correctly charged (i.e., the items that indeedhave been in the customer cart) to the value of the whole cart whichcould include the value of items that was not in the customer cart. Thisfraction would be between zero to 1 corresponding to completedissatisfaction to true satisfaction.

The VL * is the average value of the items for which the customer havenot been charged but indeed were in his cart to the whole sale valueover large enough realization of the problem.

The objective function of such optimization problem can be defined forexample as a linear combination of the vendor loss VL * and customerdissatisfaction CDS *, where classifier can be designed to minimize boththe vendor loss VL * and the customer dissatisfaction CDS *.

In various embodiments, metadata and/or inventory tracking can be usedto improve the checkout system and checkout efficiency. Otherinformation that is available about the item can be used within thecheckout process or even after the checkout process in the store iscompleted. Such information could be available through a tracking tagwithin the store before the customer begins the checkout process. Suchtracking may also be realized through grouping the tags based on theirproximity, for example, the items that are in the same shopping cart.

In various embodiments, time series analysis can be used, where, forexample, if an item is seen before the checkout area within the insideof the store (e.g., in the WA) before the customer checks out, thenwithin the checkout area during the checkout process, for a specificcustomer, and then after the checkout process in the store area betweenthe checkout area and the store exit, then such transition can be usedto assign the item to a particular customer.

Corrections to checkout can be accomplished, for example, by performingperiodic inventory either dynamically during the operation time of thestore or after the store closing. For example, if an item is assigned toa particular customer cart but later found in the store during theinventory, this item can be taken off of the customer charge and anadjustment can be made.

Multiple scenarios for keeping the information about an item by thevendor is also possible. First, a vendor would add the ID of all thetags for the items that are present in the store, which could beavailable for sale, in a database and keeps them active even after it isassigned to the customer at the checkout. An active state can mean thatthe RFID tag could be discovered at later time even if the storebelieves that it has been sold and is taken out by the customer. Hence,if it is later on found in the store, adjustment to the user cart can bemade. Finding this item within the store could happen for example duringa periodic inventory. Another possibility is that the item after thecheckout process by the customer has left her bag, for example, thecustomer dropped an item, or has forgotten to take a bag with heroutside the store.

In various embodiments, the vendor may mark an item to be inactive aftera checkout process or after the final bill is sent. This means that suchitem would not be discovered by the store at later time and may not beadjusted. In an inventory mode, each tag responds with its unique IDcalled an Electronic Product Code (EPC).

In various embodiments, the vendor may consider an item to be active foran extended time after the final bill is sent. This extended time wouldhelp to yet another possibility of adjusting the bill at later timebefore customer attempts to physically go to the customer service foradjustment process. For example, consider a scenario that the final billis sent to the customer within the time interval, say 15 minutes, afterthe customer exits the store. Even though, the final adjustment to thecustomer card has been made during a time period after the customervisit to the checkout counter and the final bill has been provided tothe customer, any undercharged or overcharged items may be adjusted sayduring the inventory that is performed at night before the store opensfor the following day. Such adjustment could be still very useful insaving a customer a trip to the store to visit the customer servicedesk.

There can also be a dedicated check-in process, where a customer whowish to reenter the store while carrying at least an item that belong tothis store has to check in. The check-in process could use the samecheckout process which means that the same system can be used for both acheck-in and checkout process. To benefit from a full potential of thisscenario, the tags should remain active at least till the final bill issent to a customer. Such active period may also be extended for a timeinterval beyond the final bill. In either case, it is necessary to havea check-in process for a customer who wish to enter or reenter the storecarrying a store item which is tagged.

In various embodiments, the checkout system can be configured to notcharge a given customer for a specific item twice. This can be based onunique ID codes that individually identify an item, which the checkoutsystem can recognize as purchased and previously charged for.

As employed herein, the term “hardware processor subsystem” or “hardwareprocessor” can refer to a processor, memory, software or combinationsthereof that cooperate to perform one or more specific tasks. In usefulembodiments, the hardware processor subsystem can include one or moredata processing elements (e.g., logic circuits, processing circuits,instruction execution devices, etc.). The one or more data processingelements can be included in a central processing unit, a graphicsprocessing unit, and/or a separate processor- or computing element-basedcontroller (e.g., logic gates, etc.). The hardware processor subsystemcan include one or more on-board memories (e.g., caches, dedicatedmemory arrays, read only memory, etc.). In some embodiments, thehardware processor subsystem can include one or more memories that canbe on or off board or that can be dedicated for use by the hardwareprocessor subsystem (e.g., ROM, RAM, basic input/output system (BIOS),etc.).

In some embodiments, the hardware processor subsystem can include andexecute one or more software elements. The one or more software elementscan include an operating system and/or one or more applications and/orspecific code to achieve a specified result.

In other embodiments, the hardware processor subsystem can includededicated, specialized circuitry that performs one or more electronicprocessing functions to achieve a specified result. Such circuitry caninclude one or more application-specific integrated circuits (ASICs),field-programmable gate arrays (FPGAs), and/or programmable logic arrays(PLAs).

These and other variations of a hardware processor subsystem are alsocontemplated in accordance with embodiments of the present invention.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present invention, as well as other variations thereof, means that aparticular feature, structure, characteristic, and so forth described inconnection with the embodiment is included in at least one embodiment ofthe present invention. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment. However, it is to beappreciated that features of one or more embodiments can be combinedgiven the teachings of the present invention provided herein.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended for as many items listed.

The foregoing is to be understood as being in every respect illustrativeand exemplary, but not restrictive, and the scope of the inventiondisclosed herein is not to be determined from the Detailed Description,but rather from the claims as interpreted according to the full breadthpermitted by the patent laws. It is to be understood that theembodiments shown and described herein are only illustrative of thepresent invention and that those skilled in the art may implementvarious modifications without departing from the scope and spirit of theinvention. Those skilled in the art could implement various otherfeature combinations without departing from the scope and spirit of theinvention. Having thus described aspects of the invention, with thedetails and particularity required by the patent laws, what is claimedand desired protected by Letters Patent is set forth in the appendedclaims.

What is claimed is:
 1. A system for self-checkout at a point-of-sale,comprising: a plurality of radio frequency identification (RFID)transceivers within a store; an RFID reader configured to receive anRFID code from an RFID tag activated by the plurality of radio frequencyidentification (RFID) transceivers; and a classifier configured todetermine whether the RFID tag is inside or outside a designated area,wherein the classifier is trained in a manner that a number of itemsincorrectly identified as being purchased is below a threshold tominimize customer dissatisfaction (CDS) determined as the ratio of thevalue of items charged to the customer but not purchased by a customerto the total charge to the customer.
 2. The system as recited in claim1, wherein the classifier minimizes a combination of a vendor loss (VL)determined as a ratio of the value of items that exit the store withoutbeing charged to the customer to the total amount of the value of theitems taken from the store and paid for plus the value of the itemscharged to the customer, but not actually purchased, and the customerdissatisfaction (CDS).
 3. The system as recited in claim 1, wherein thecharge to the customer is adjusted after the customer has exited thestore.
 4. The system as recited in claim 3, wherein the adjustment tothe charge is determined by a subsequent inventory of store items. 5.The system as recited in claim 3, wherein the designated area is a storecheckout area (CA) determined by the signal coverage of the plurality ofradio frequency identification (RFID) transceivers and a pair ofRF-absorbing walls separated by a distance.
 6. The system as recited inclaim 5, further comprising a waiting area (WA) adjoining the checkoutarea, where at least a subset of the plurality of radio frequencyidentification (RFID) transceivers are oriented to send signals to andreceive signals from the waiting area (WA).
 7. The system as recited inclaim 6, further comprising entrance sensors at an entrance side of thepair of RF-absorbing walls configured to determine that a person hasentered the checkout area, wherein the RFID reader initiates a checkoutprocess by sending an energizing signal from the plurality of radiofrequency identification (RFID) transceivers.
 8. The system as recitedin claim 7, further comprising a billing terminal with a paymentprocessor configured to receive payment information from the customer.9. The system as recited in claim 8, further comprising exit sensors atan exit side of the pair of RF-absorbing walls configured to determinethat a person has exited the checkout area.
 10. A system forself-checkout at a point-of-sale, comprising: a pair of RF-absorbingwalls separated by a distance; a plurality of radio frequencyidentification (RFID) elevated transceivers within each of the pair ofRF-absorbing walls; a plurality of floor-mounted transceivers in a floorbelow the pair of RF-absorbing walls, wherein the signal coverage of theplurality of radio frequency identification (RFID) transceiversdetermines a checkout area (CA); an RFID reader, wherein the RFID readeris configured to receive an RFID code from each of a plurality of RFIDtags activated simultaneously by the plurality of radio frequencyidentification (RFID) transceivers, the RFID reader configured toreceive each of the plurality of RFID codes in a separate time slot of ablock of a transmit frame; and a classifier configured to determinewhether the RFID tag is inside or outside a designated area, wherein theclassifier is trained in a manner that a number of items incorrectlyidentified as being purchased is below a threshold to minimize customerdissatisfaction (CDS) determined as the ratio of the value of itemscharged to the customer but not purchased by a customer to the totalcharge to the customer.
 11. The system as recited in claim 10, whereinthe classifier is a neural network model that applies weights to aninput feature vector and outputs a probability for each RFID tag beinginside the designated area.
 12. The system as recited in claim 11,further comprising a waiting area (WA) adjoining the checkout area,where at least a subset of the plurality of radio frequencyidentification (RFID) transceivers are oriented to send signals to andreceive signals from the waiting area (WA).
 13. The system as recited inclaim 12, wherein the classifier minimizes a combination of a vendorloss (VL) determined as a ratio of the value of items that exit thestore without being charged to the customer to the total amount of thevalue of the items taken from the store and paid for plus the value ofthe items charged to the customer, but not actually purchased, and thecustomer dissatisfaction (CDS).
 14. The system as recited in claim 13,wherein the charge to the customer is adjusted after the customer hasexited the store.
 15. A method for self-checkout at a point-of-sale,comprising: detecting that a person has entered a checkout area;determining whether an RFID tag is inside the checkout area (CA) using aneural network classifier and a plurality of radio frequencyidentification (RFID) transceivers; and charging the person an amountfor items associated with the RFID tag determined to be inside thecheckout area.
 16. The method as recited in claim 15, wherein theclassifier minimizes a combination of a vendor loss (VL) determined as aratio of the value of items that exit the store without being charged tothe customer to the total amount of the value of the items taken fromthe store and paid for plus the value of the items charged to thecustomer, but not actually purchased, and the customer dissatisfaction(CDS) determined as the ratio of the value of items charged to thecustomer but not purchased by a customer to the total charge to thecustomer.
 17. The method as recited in claim 15, further comprisingdetermining that the person has exited a checkout area.
 18. The methodas recited in claim 17, wherein the person is charged for the itemsassociated with the plurality of RFID tags after the person has left thecheckout area.
 19. The method as recited in claim 18, wherein the chargeto the customer is adjusted after the customer has exited the store. 20.The method of claim 19, wherein the adjustment to the charge isdetermined by a subsequent inventory of store items.